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Controlled showering optimization algorithm: an intelligent tool for decision making in global optimization

Author

Listed:
  • Javaid Ali

    (University of Management and Technology)

  • Muhammad Saeed

    (University of Management and Technology)

  • Muhammad Farhan Tabassam

    (University of Management and Technology)

  • Shaukat Iqbal

    (University of Management and Technology)

Abstract

In this study a novel population based meta-heuristic, called controlled showering optimization (CSO) algorithm, for global optimization of unconstrained problems is presented. Modern irrigation systems are equipped with smart tools manufactured and controlled by human intelligence. The proposed CSO algorithm is inspired from the functioning of water distribution tools to model search agents for carrying out the optimization process. CSO imitates the mechanism of projection of water units by sprinklers and the movements of their platforms to the desired locations for scheming optimum searching procedures. The proposed method has been tested using a number of diverse natured benchmark functions with low and high dimensions. Statistical analysis of the empirical data demonstrates that CSO offers solutions of better quality in comparison with several well-practiced algorithms like genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), artificial bee colony (ABC), covariance matrix adaptation evolution strategy (CMA-ES), teaching and learning based optimization (TLBO) and water cycle algorithm (WCA). The experiments on high-dimensional problems reveal that CSO algorithm also outperforms significantly a number of algorithms designed specifically for high dimensional global optimization problems.

Suggested Citation

  • Javaid Ali & Muhammad Saeed & Muhammad Farhan Tabassam & Shaukat Iqbal, 2019. "Controlled showering optimization algorithm: an intelligent tool for decision making in global optimization," Computational and Mathematical Organization Theory, Springer, vol. 25(2), pages 132-164, June.
    • Handle: RePEc:spr:comaot:v:25:y:2019:i:2:d:10.1007_s10588-019-09293-6
    DOI: 10.1007/s10588-019-09293-6
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    References listed on IDEAS

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    1. Lina Zhang & Liqiang Liu & Xin-She Yang & Yuntao Dai, 2016. "A Novel Hybrid Firefly Algorithm for Global Optimization," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-17, September.
    2. I. D. Coope & C. J. Price, 2000. "Frame Based Methods for Unconstrained Optimization," Journal of Optimization Theory and Applications, Springer, vol. 107(2), pages 261-274, November.
    3. Garg, Harish, 2016. "A hybrid PSO-GA algorithm for constrained optimization problems," Applied Mathematics and Computation, Elsevier, vol. 274(C), pages 292-305.
    4. Mostafa Z. Ali & Ayad Salhieh & Randa T. Abu Snanieh & Robert G. Reynolds, 2012. "Boosting cultural algorithms with a heterogeneous layered social fabric influence function," Computational and Mathematical Organization Theory, Springer, vol. 18(2), pages 193-210, June.
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    Cited by:

    1. Ali, Javaid & Raza, Ali & Ahmed, Nauman & Ahmadian, Ali & Rafiq, Muhammad & Ferrara, Massimiliano, 2021. "Evolutionary optimized Padé approximation scheme for analysis of covid-19 model with crowding effect," Operations Research Perspectives, Elsevier, vol. 8(C).

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